Statistics (Discrete and Continuous)
20
CM0123L
2015/6
Semester 1
A
FHEQ Level 4
Linked 10+10
Computer Science
School of Computing, Informatics and Media (Mathematics)
Dr A Csenki

None
None
To present an introduction to the principle of randomness through event theory, random samples and discrete variables. To extend the concepts to continuous random variables, estimation and sampling, and elementary statistical inference.
Formative assessment assignments encourage the ongoing digestion of the material, with the extent of the cumulative knowledge and skills acquired assessed through two coursework assignments and a formal examination.
Study Hours:  
Lectures:  36.00  Directed Study:  137.50  
Seminars/Tutorials:  24.00  Other:  0.00  
Laboratory/Practical:  0.00  Formal Exams:  2.50  Total: 200.00 
On successful completion of this module you will be able to...
show an understanding of the fundamentals of event theory, random samples and discrete variables, and the basic principles involved in using continuous random variables.
On successful completion of this module you will be able to...
manipulate using the basic principles of event theory and discrete random variables, and apply the underlying properties of mathematical expectation, sample analysis, regression modelling, estimation, sampling and statistical inference.
On successful completion of this module you will be able to...
learn and work independently with patience and persistence using good general skills of organization and timemanagement, write coherently and clearly communicate results.
001.  Assessment Type  Duration  Percentage 
Coursework  25%  
Description  
2 assignments consisting of questions taking approximately 2 hours to answer per assignment  
002.  Assessment Type  Duration  Percentage 
Examination  closed book  2.50  75%  
Description  
Examination  
900.  Assessment Type  Duration  Percentage 
Examination  closed book  3.00  100%  
Description  
Supplementary examination 
CONTINUOUS RANDOM VARIABLES: p.d.f.; expectation; moments (central, noncentral); m.g.f.; exponential and uniform distributions applications; gamma integral; gamma distribution; Weibull distribution. GAUSSIAN NORMAL FAMILY: standard tables; standardization; variables transformation; normal m.g.f.; normal approximation to binomial and Poisson distributions. BIVARIATE DISTRIBUTIONS (discrete): expectation of H(X,Y); covariance; independent random variables; m.g.f. independent random variables sums. ESTIMATION: point; maximum likelihood; moments method; unbiased; consistency; population variance unbiased estimator; confidence intervals; normal population mean.
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